Knowledge-Guided Disentangled Representation Learning for Recommender Systems

In recommender systems, it is essential to understand the underlying factors that affect user-item interaction. Recently, several studies have utilized disentangled representation learning to discover such hidden factors from user-item interaction data, which shows promising results. However, without any external guidance signal, the learned disentangled representations lack clear meanings, and are easy to suffer from the data sparsity issue. In light of these challenges, we study how to leverage knowledge graph (KG) to guide the disentangled representation learning in recommender systems. The purpose for incorporating KG is twofold, making the disentangled representations interpretable and resolving data sparsity issue. However, it is not straightforward to incorporate KG for improving disentangled representations, because KG has very different data characteristics compared with user-item interactions. We propose a novel K nowledge-guided D isentangled R epresentations approach ( KDR ) to utilizing KG to guide the disentangled representation learning in recommender systems. The basic idea, is to first learn more interpretable disentangled dimensions (explicit disentangled representations) based on structural KG, and then align implicit disentangled representations learned from user-item interaction with the explicit disentangled representations. We design a novel alignment strategy based on mutual information maximization. It enables the KG information to guide the implicit disentangled representation learning, and such learned disentangled representations will correspond to semantic information derived from KG. Finally, the fused disentangled representations are optimized to improve the recommendation performance. Extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed model in terms of both performance and interpretability.

Crossmodality Person Reidentification Based on Global and Local Alignment

RGB-infrared (RGB-IR) person reidentification is a challenge problem in computer vision due to the large crossmodality difference between RGB and IR images. Most traditional methods only carry out feature alignment, which ignores the uniqueness of modality differences and is difficult to eliminate the huge differences between RGB and IR. In this paper, a novel AGF network is proposed for RGB-IR re-ID task, which is based on the idea of global and local alignment. The AGF network distinguishes pedestrians in different modalities globally by combining pixel alignment and feature alignment and highlights more structure information of person locally by weighting channels with SE-ResNet-50, which has achieved ideal results. It consists of three modules, including alignGAN module ( A ), crossmodality paired-images generation module ( G ), and feature alignment module ( F ). First, at pixel level, the RGB images are converted into IR images through the pixel alignment strategy to directly reduce the crossmodality difference between RGB and IR images. Second, at feature level, crossmodality paired images are generated by exchanging the modality-specific features of RGB and IR images to perform global set-level and fine-grained instance-level alignment. Finally, the SE-ResNet-50 network is used to replace the commonly used ResNet-50 network. By automatically learning the importance of different channel features, it strengthens the ability of the network to extract more fine-grained structural information of person crossmodalities. Extensive experimental results conducted on SYSU-MM01 dataset demonstrate that the proposed method favorably outperforms state-of-the-art methods. In addition, we evaluate the performance of the proposed method on a stronger baseline, and the evaluation results show that a RGB-IR re-ID method will show better performance on a stronger baseline.

What is the “safe zone” for transition of coronal alignment from systematic to a more personalised one in total knee arthroplasty? A systematic review

Purpose In total knee arthroplasty (TKA), implants are increasingly aligned based on emerging patient-specific alignment strategies, such as unrestricted kinematic alignment (KA), according to their constitutional limb alignment (phenotype alignment), which results in a large proportion of patients having a hip-knee angle (HKA) outside the safe range of ± 3° to 180° traditionally considered in the mechanical alignment strategy. The aim of this systematic review is to investigate whether alignment outside the safe zone of ± 3° is associated with a higher revision rate and worse clinical outcome than alignment within this range. Methods A systematic literature search was conducted in PubMed, Embase, Cochrane and World of Science, with search terms including synonyms and plurals for “total knee arthroplasty”, “alignment”, “outlier”, “malalignment”, “implant survival” and “outcome”. Five studies were identified with a total number of 927 patients and 952 implants. The Oxford Knee Score (OKS) and the WOMAC were used to evaluate the clinical outcome. The follow-up period was between 6 months and 10 years. Results According to HKA 533 knees were aligned within ± 3°, 47 (8.8%) were varus outliers and 121 (22.7%) were valgus outliers. No significant differences in clinical outcomes were found between implants positioned within ± 3° and varus and valgus outliers. Likewise, no significant differences were found regarding revision rates and implant survival. Conclusion The universal use of the “safe zone” of ± 3° derived from the mechanical alignment strategy is hardly applicable to modern personalised alignment strategies in the light of current literature. However, given the conflicting evidence in the literature on the risks of higher revision rates and poorer clinical outcomes especially with greater tibial component deviation, the lack of data on the outcomes of more extreme alignments, and regarding the use of implants for KA TKA that are actually designed for mechanical alignment, there is an urgent need for research to define eventual evidence-based thresholds for new patient-specific alignment strategies, not only for HKA but also for FMA and TMA, also taking into account the preoperative phenotype and implant design. It is of utmost clinical relevance for the application of modern alignment strategies to know which native phenotypes may be reproduced with a TKA. Level of evidence IV.

Identifying proximal RNA interactions from cDNA-encoded crosslinks with ShapeJumper

SHAPE-JuMP is a concise strategy for identifying close-in-space interactions in RNA molecules. Nucleotides in close three-dimensional proximity are crosslinked with a bi-reactive reagent that covalently links the 2’-hydroxyl groups of the ribose moieties. The identities of crosslinked nucleotides are determined using an engineered reverse transcriptase that jumps across crosslinked sites, resulting in a deletion in the cDNA that is detected using massively parallel sequencing. Here we introduce ShapeJumper, a bioinformatics pipeline to process SHAPE-JuMP sequencing data and to accurately identify through-space interactions, as observed in complex JuMP datasets. ShapeJumper identifies proximal interactions with near-nucleotide resolution using an alignment strategy that is optimized to tolerate the unique non-templated reverse-transcription profile of the engineered crosslink-traversing reverse-transcriptase. JuMP-inspired strategies are now poised to replace adapter-ligation for detecting RNA-RNA interactions in most crosslinking experiments.

Identifying proximal RNA interactions from cDNA-encoded crosslinks with ShapeJumper

SHAPE-JuMP is a concise strategy for identifying close-in-space interactions in RNA molecules. Nucleotides in close three-dimensional proximity are crosslinked with a bi-reactive reagent that covalently links the 2'-hydroxyl groups of the ribose moieties. The identities of crosslinked nucleotides are determined using an engineered reverse transcriptase that jumps across crosslinked sites, resulting in a deletion in the cDNA that is detected using massively parallel sequencing. Here we introduce ShapeJumper, a bioinformatics pipeline to process SHAPE-JuMP sequencing data and to accurately identify through-space interactions. ShapeJumper identifies proximal interactions with near-nucleotide resolution using an alignment strategy that is optimized to tolerate the unique non-templated reverse-transcription profile of the engineered crosslink-traversing reverse-transcriptase. JuMP-inspired strategies are now poised to replace adapter-ligation for detecting RNA-RNA interactions in most crosslinking experiments.

Intra-Regional Alignment Strategy of Small States in Gulf area: Cases of Qatar and United Arab Emirates (UAE)

The Middle East may not possess any great power, but the region has, within itself, several states that hold relatively bigger capabilities and resources compared to the others. Saudi Arabia, Iran, Israel, Egypt, and previously Iraq, are competing to be the main stakeholder in this highly chaotic region. This paper attempts to do a comparative analysis on two gulf countries: Qatar and United Arab Emirates (UAE). The two countries are selected as both are the key economic and strategic players among the small states in the Middle East. The intra-regional alignment behavior of Qatar and UAE as small states in Middle East proves that even in a region where alignments are multi-layered (with intra-regional powers and with international big powers), small states alignment behavior is heavily driven by the intention of minimalizing threats, if not to diminish it completely. Such behavior is expected from states that feel vulnerable within the anarchical environment externally. The external threats and uncertainties, however, are selected and faced according to how the ruling elite perceives it. To preserve domestic political legitimacy, threats and alignment choices become the useful cards for the ruling elites to show their authority, performance, and stature to their domestic audience.

Practical Aspects of Propulsion Shaft Alignment

The economical and operational implications of poor alignment are indisputable for the propulsion shafting system of a commercial vessel. This holds true for naval vessels as well, although far less documented in the technical literature. This paper addresses some of the challenges associated with the proper alignment of a high-speed naval craft, which has been in service for many years. Laser bore-sighting was performed on a Guided Missile Fast Patrol Boat resting on a docking cradle. The measured bearing offsets were input to a FEA model of the shafting system to calculate bearing reactions and detect potential misalignment issues. Subsequent decisions regarding corrective measures take into account the results computed by the numerical model, experience from sister ships, the available documentation from the building yard and several other factors which are discussed in the paper. The solutions proposed are targeted towards a balanced trade-off between cost effectiveness and out-of-service time on one hand, and the risk of potential damage from misalignment on the other hand, which would seriously disrupt the ship’s operational availability. Practical aspects and lessons identified in the process are also presented, which demonstrate the distinct differences in alignment strategy of a high-speed naval craft compared to a typical commercial vessel.

Decoupling alignment strategy from feature quantification using a standard alignment incidence data structure

SummaryThe abundance of genomic feature such as gene expression is often estimated from observed total number of alignment incidences in the targeted genome regions. We introduce a generic data structure and associated file format for alignment incidence data so that method developers can create novel pipelines comprising models, each optimal for read alignment, post-alignment QC, and quantification across multiple sequencing modalities.Availability and Implementationalntools software is freely available at https://github.com/churchill-lab/alntools under MIT [email protected] or [email protected]